SRVoice: A Robust Sparse Representation-Based Liveness Detection System

Author(s):  
Jiacheng Shang ◽  
Si Chen ◽  
Jie Wu
2021 ◽  
Vol 1871 (1) ◽  
pp. 012046
Author(s):  
Ling Yue ◽  
Chenhong Cao ◽  
Yufeng Li ◽  
Jiangtao Li ◽  
Qi Liu

2019 ◽  
Vol 125 ◽  
pp. 71-77 ◽  
Author(s):  
Eric-Juwei Cheng ◽  
Kuang-Pen Chou ◽  
Shantanu Rajora ◽  
Bo-Hao Jin ◽  
M. Tanveer ◽  
...  

2016 ◽  
Vol 10 (1) ◽  
pp. 361-374
Author(s):  
Xu Guang Zhu ◽  
Yin Pan Long ◽  
Lei Bang Jun ◽  
Zou Yao Bin ◽  
Yang Ji Quan

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Changjun Zha

Wireless sensor networks (WSNs) suffer from limited power and large amounts of redundant data. This paper describes a multisource data fusion method for WSNs that can be combined with the characteristics of a profile detection system. First, principal component analysis is used to extract sample features and eliminate redundant information. Feature samples from different sources are then fused using a method of superposition to reduce the amount of data transmitted by the network. Finally, a mathematical model is proposed. On the basis of this model, a novel method of special object recognition based on sparse representation is developed for multisource data fusion samples according to the distribution of nonzero coefficients under an overcomplete dictionary. The experimental results from numerical simulations show that the proposed recognition method can effectively identify special objects in the fusion samples, and the overall performance is better than that of traditional methods.


2020 ◽  
Vol 34 (05) ◽  
pp. 2030001 ◽  
Author(s):  
Rohit Agarwal ◽  
A. S. Jalal ◽  
K. V. Arya

Fingerprint recognition systems are susceptible to artificial spoof fingerprint attacks, like molds manufactured from polymer, gelatin or Play-Doh. Presentation attack is an open issue for fingerprint recognition systems. In a presentation attack, synthetic fingerprint which is reproduced from a real user is submitted for authentication. Different sensors are used to capture the live and fake fingerprint images. A liveness detection system has been designed to defeat different classes of spoof attacks by differentiating the features of live and fake fingerprint images. In the past few years, many hardware- and software-based approaches are suggested by researchers. However, the issues still remain challenging in terms of robustness, effectiveness and efficiency. In this paper, we explore all kinds of software-based solution to differentiate between real and fake fingerprints and present a comprehensive survey of efforts in the past to address this problem.


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